Dynamic optimization of a copolymerization reactor using tabu search

Area of Science:

• Chemical Engineering
• Polymer Science
• Process Control

Background:

• Semi-batch copolymerization reactors require precise control for product quality.
• Dynamic optimization is crucial for managing transient processes.
• Meta-heuristic algorithms offer advanced solutions for complex optimization problems.

Purpose of the Study:

• To propose and evaluate a novel multistage dynamic optimization strategy using tabu search (TS).
• To apply the TS strategy to a semi-batch styrene-acrylonitrile (SAN) copolymerization reactor.
• To compare the TS strategy with conventional iterative dynamic programming (IDP).

Main Methods:

• Development of a multistage dynamic optimization strategy based on tabu search (TS).
• Implementation of TS through sequential and simultaneous procedures.
• Optimization of temperature and monomer addition rate for product quality objectives.
• Comparison with iterative dynamic programming (IDP).

Main Results:

• The TS-based strategies achieved optimal control policies for temperature and monomer addition.
• Both sequential and simultaneous TS implementations demonstrated effectiveness.
• Simulation results showed TS is useful for optimizing transient dynamic systems.
• TS performance was comparable or superior to IDP.

Conclusions:

• The proposed TS dynamic optimization strategy is effective for controlling SAN copolymerization.
• TS offers adaptive memory and responsive exploration for complex control problems.
• The strategy is valuable for optimizing transient dynamic systems in chemical processes.